#We start by loading our packages
library(rayshader)
library(rgl)
library(av)
library(gifski)
library(magick) #The first five are just for the 3d-model gif.
library(ggplot2)
library(dplyr)
library(magrittr) #should be included in dplyr, but didn't work?
library(plotly) #For interactive ggplot
library(hrbrthemes) #For better ggplot-themes
#First we load our data into our environment
library(tidytuesdayR) #Specific package for loading tidy-tuesday data
tuesdata <- tidytuesdayR::tt_load('2025-06-24')
cases_month <- tuesdata$cases_month #Loading datasets into environment
cases_year <- tuesdata$cases_year #Loading datasets into environment
dim(cases_year)
## [1] 2382 19
#Looking at variable names
names(cases_month)
## [1] "region" "country" "iso3"
## [4] "year" "month" "measles_suspect"
## [7] "measles_clinical" "measles_epi_linked" "measles_lab_confirmed"
## [10] "measles_total" "rubella_clinical" "rubella_epi_linked"
## [13] "rubella_lab_confirmed" "rubella_total" "discarded"
names(cases_year)
## [1] "region"
## [2] "country"
## [3] "iso3"
## [4] "year"
## [5] "total_population"
## [6] "annualized_population_most_recent_year_only"
## [7] "total_suspected_measles_rubella_cases"
## [8] "measles_total"
## [9] "measles_lab_confirmed"
## [10] "measles_epi_linked"
## [11] "measles_clinical"
## [12] "measles_incidence_rate_per_1000000_total_population"
## [13] "rubella_total"
## [14] "rubella_lab_confirmed"
## [15] "rubella_epi_linked"
## [16] "rubella_clinical"
## [17] "rubella_incidence_rate_per_1000000_total_population"
## [18] "discarded_cases"
## [19] "discarded_non_measles_rubella_cases_per_100000_total_population"
#Recoding regions for OUR understanding
cases_year <- cases_year %>%
mutate(region = recode(region, `AFRO` = "African Region", `AMRO` = "Region of Americas", `EMRO` = "Eastern Mediterraenean Region", `EURO` = "European Region", `SEARO` = "Sout-East Asian Region", `WPRO` = "Western Pacific Region"))
cases_month <- cases_month %>%
mutate(region = recode(region, `AFR` = "African Region", `AMR` = "Region of Americas", `EMR` = "Eastern Mediterraenean Region", `EUR` = "European Region", `SEAR` = "Sout-East Asian Region", `WPR` = "Western Pacific Region"))
unique(cases_month$region)
## [1] "African Region" "Region of Americas"
## [3] "Eastern Mediterraenean Region" "European Region"
## [5] "Sout-East Asian Region" "Western Pacific Region"
#First we want to look at the overall average incidence rate pr. year worldwide
cases_year %>%
group_by(year) %>% #Grouping by year
mutate(avg_inc = mean(measles_incidence_rate_per_1000000_total_population)) %>% #Creating new variable with mutate
ggplot(aes(x = year, y = avg_inc)) + #inserting mapping-argument in overall ggplot for less typing
geom_point() +
theme_bw() +
labs(title = "Avg. measles incidence rate worldwide \n(pr. 1,000,000)", x = "Year", y = "Avg. Incidence Rate") + #Notice the \n for changing #line
geom_smooth() +
geom_text(aes(label = round(avg_inc)), vjust = 1.5, size = 3) #Putting avg_incidence text for each point and adjusting size and position
#Now we want to facet by region. We supress warnings and messages in the output in the first row.
plotfacet <- cases_year %>%
group_by(year, region) %>% #We group by year AND region so we get an annual avg. pr. region
mutate(avg_reg = mean(measles_incidence_rate_per_1000000_total_population)) %>%
ggplot() +
geom_point(aes(x = year, y = avg_reg, colour = region, shape = region)) +
theme_bw() +
labs(title = "Avg. measles incidence rate pr. region pr. year(pr. 1,000,000)", x = "Year", y = "Avg. Incidence Rate", color = "Region", shape = "Region") +
geom_smooth(aes(x = year, y = avg_reg), color = "grey", se=FALSE, show.legend = FALSE) +
facet_wrap(~ region, scales = "free") +
geom_line(aes(x = year, y = avg_reg, colour = region, shape = region)) +
theme(strip.text = element_text(
color = "white"),
strip.background = element_rect(
fill = "grey20")) #Changing the top bar colour and the text color
ggplotly(plotfacet)
cases_year1 <- cases_year %>%
group_by(year, region) %>%
mutate(avg_reg = mean(measles_incidence_rate_per_1000000_total_population))
hexgg <- ggplot(cases_year1, aes(x = year, y = avg_reg)) +
stat_bin_hex(aes(fill = after_stat(density), colour = after_stat(density)),
bins = 10,
linewidth = 1) +
scale_fill_viridis_c(option = "B") +
scale_color_viridis_c(option = "B", guide = "none") +
labs(x = "Year", y = "Avg. incidence rate pr. region", fill = "",
colour = "") +
theme_minimal()
hexgg
plot_gg(hexgg, multicore = TRUE, windowsize = c(800, 800))
render_movie("silly.gif")
knitr::include_graphics('C:/Users/au483794/OneDrive - Aarhus universitet/Documents/Fridayproject/Peter/silly.gif')
